Optimized stochastic resonance method for signal detection and image processing

ABSTRACT

Apparatus and method for improving the detection of signals obscured by noise using stochastic resonance noise. The method determines the stochastic resonance noise probability density function in non-linear processing applications that is added to the observed data for optimal detection with no increase in probability of false alarm. The present invention has radar, sonar, signal processing (audio, image and video), communications, geophysical, environmental, and biomedical applications.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Application No. 60/728,504, filed Oct. 20, 2006.

The U.S. Government has a paid-up license in this invention and the right in limited circumstances to require the patent owner to license others on reasonable terms as provided for by the terms of Contract No. FA9550-05-C-0139 awarded by the Air Force Office of Scientific Research (AFOSR).

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to signal detection and, more particularly, to a method of detecting signals and processing images using stochastic resonance.

2. Description of the Related Art

Stochastic resonance (SR) is a nonlinear physical phenomenon in which the output signals of some nonlinear systems can be enhanced by adding suitable noise under certain conditions. The classic SR signature is the signal-to-noise ratio (SNR) gain of certain nonlinear systems, i.e., the output SNR is higher than the input SNR when an appropriate amount of noise is added.

Although SNR is a very important measure of system performance, SNR gain-based SR approaches have several limitations. First, the definition of SNR is not uniform and it varies from one application to another. Second, to optimize the performance, the complete a priori knowledge of the signal is required. Finally, for detection problems where the noise is non-Gaussian, SNR is not always directly related to detection performance; i.e., optimizing output SNR does not guarantee optimizing probability of detection.

In signal detection theory, SR also plays a very important role in improving the signal detectability. For example, improvement of detection performance of a weak sinusoid signal has been reported. To detect a DC signal in a Gaussian mixture noise background, performance of the sign detector can be enhanced by adding some white Gaussian noise under certain circumstances. For the suboptimal detector known as the locally optimal detector (LOD), detection performance is optimum when the noise parameters and detector parameters are matched. The stochastic resonance phenomenon in quantizers results in a better detection performance can be achieved by a proper choice of the quantizer thresholds. Detection performance can be further improved by using an optimal detector on the output signal. Despite the progress achieved by the above approaches, the use the SR effect in signal detection systems is rather limited and does not fully consider the underlying theory of SR.

Simple and robust suboptimal detectors are used in numerous applications. To improve a suboptimal detector detection performance, two approaches are widely used. In the first approach, the detector parameters are varied. Alternatively, when the detector itself cannot be altered or the optimum parameter values are difficult to obtain, adjusting the observed data becomes a viable approach. Adding a dependent noise is not always possible because pertinent prior information is usually not available.

For some suboptimal detectors, detection performance can be improved by adding an independent noise to the data under certain conditions. For a given type of SR noise, the optimal amount of noise can be determined that maximizes the detection performance for a given suboptimal detector. However, despite the progress made, the underlying mechanism of the SR phenomenon as it relates to detection problems has not fully been explored. For example, until now the “best” noise to be added in order to achieve the best achievable detection performance for the suboptimal detector was not known. Additionally, the optimal level of noise that should be used for enhanced performance was also unknown.

BRIEF SUMMARY OF THE INVENTION

It is therefore a principal object and advantage of the present invention to provide a method for determining the best noise to add to improve detection of a suboptimal, non-linear detector.

It is an additional object and advantage of the present invention to provide a method for determining the optimal level of noise for improved detection.

In accordance with the foregoing objects and advantages, the present invention provides a method for signal detection in observed sensor data for a broad range of electromagnetic or acoustic applications such as radar, sonar, as well as imagery such as visual, hyperspectral, and multi-spectral. The method of the present invention is applicable in applications involving non-linear processing of the data. Specifically, the method of the present invention determines the stochastic resonance noise probability density function to be added to either the observed data process to optimize detection with no increase in the false alarm rate, or to an image to optimize the detection of signal objects from the background. In addition, the method of the present invention determines the conditions required for performance improvement using additive stochastic resonance noise. The method of the present invention also yields a constant false alarm rate (CFAR) receiver implementation, which is essential in operational conditions in which it is imperative to maintain false alarm rates without adjusting the detector threshold level.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be more fully understood and appreciated by reading the following Detailed Description in conjunction with the accompanying drawings, in which:

FIG. 1 is a graph of the effect of additive noise according to the present invention.

FIG. 2 is a graph of the values of F₁ and F₀ as a function of x according to the present invention.

FIG. 3 is a graph of the relationship between f₁ and f₀ according to the present invention.

FIG. 4 is a graph of the relationship between G(f₀; k), f₀, f_(0i)(k), v_(i)(k) with i=1, 2 and different k value 0, 1 and 2 according to the present invention.

FIG. 5 is a graph illustrating different H(x) curves where μ=3, A=1 according to the present invention.

FIG. 6 is a graph of P_(D) ^(y) as a function of signal level A in Gaussian mixture noise when μ=3 and σ₀=1 according to the present invention.

FIG. 7 is a graph of P_(D) ^(y) as a function of σ₀ for different types of noise enhanced detectors when μ=3 and A=1 according to the present invention.

FIG. 8 is a graph of P_(D) ^(y) as a function of μ for different types of noise enhanced detectors when σ₀=1 and A=1 according to the present invention.

FIG. 9 is a graph of the ROC curves for different SR noise enhanced sign detectors when N=30 according to the present invention.

FIG. 10 is a schematic of an SR detection system according to the present invention.

FIG. 11 is a schematic of an SR detection system according to the present invention

DETAILED DESCRIPTION OF THE INVENTION

Referring now to the drawings, wherein like reference numerals refer to like parts throughout, there is seen in FIG. 1 a chart illustrating the effective of additive noise on a given signal.

The following definitions serve to clarify the present invention:

The term “constant false alarm rate” (CFAR) refers to the attribute of a receiver that maintains the false alarm rate fixed in the presence of changing interference levels.

The term “false alarm” refers to the decision that a signal is present when in fact it is not.

The term “false alarm rate” refers to the rate at which a false alarm occurs.

The term “fixed detector” refers to a detector comprised of a fixed test statistic and a fixed threshold.

The term “receiver operating characteristic” (ROC) refers to a plot of the probability of detection as a function of the probability of false alarm for a given detector.

To enhance the detection performance, noise is added to an original data process x to obtain a new data process y given by y=x+n, where n is either an independent random process with pdf p_(n)(•) or a nonrandom signal. There is no constraint on n. For example, n can be white noise, colored noise, or even be a deterministic signal A, corresponding to p_(n)(n)=δ(n−A). As described herein, depending on the detection problem, an improvement of detection performance may not always be possible. In that case, the optimal noise is equal to zero. The pdf of y is expressed by the convolutions of the pdfs such that p _(y)(y)=p _(x)(x)*p _(n)(x)=∫_(R) _(N) p _(x)(x)p _(n)(y−x)dx)  (7)

The binary hypotheses testing problem for this new observed data y can be expressed as:

$\begin{matrix} \left\{ \begin{matrix} {{H_{0}\text{:}\mspace{14mu}{p_{y}\left( {y;H_{0}} \right)}} = {\int_{R^{N}}{{p_{0}(x)}{p_{n}\left( {y - x} \right)}{\mathbb{d}x}}}} \\ {{H_{1}\text{:}\mspace{14mu}{p_{y}\left( {y;H_{1}} \right)}} = {\int_{R^{N}}{{p_{1}(x)}{p_{n}\left( {y - x} \right)}{\mathbb{d}x}}}} \end{matrix} \right. & (8) \end{matrix}$

Since the detector is fixed, i.e., the critical function φ of y is the same as that for x, the probability of detection based on data y is given by,

$\begin{matrix} {\begin{matrix} {P_{D}^{y} = {\int_{R^{N}}{{\phi(y)}{p_{y}\left( {y;H_{1}} \right)}{\mathbb{d}y}}}} \\ {= {\int_{R^{N}}{{\phi(y)}{\int_{R^{N}}{{p_{1}(x)}{p_{n}\left( {y - x} \right)}{\mathbb{d}x}{\mathbb{d}y}}}}}} \\ {= {\int_{R^{N}}{{p_{1}(x)}\left( {\int_{R^{N}}{{\phi(y)}{p_{n}\left( {y - x} \right)}{\mathbb{d}y}}} \right){\mathbb{d}x}}}} \\ \left. {= {{\int_{R^{N}}{{p_{1}(x)}{C_{n,\phi}(x)}{\mathbb{d}x}}} = {E_{1}\left\lbrack {C_{n,\phi}(x)} \right\rbrack}}} \right) \end{matrix}{Where}} & (9) \\ {{C_{n,\phi}(x)} \equiv {\int_{R^{N}}{{\phi(y)}{p_{n}\left( {y - x} \right)}{\mathbb{d}y}}}} & (10) \end{matrix}$

Alternatively,

$\begin{matrix} \begin{matrix} {P_{D}^{y} = {\int_{R^{N}}{{p_{n}(x)}\left( {\int_{R^{N}}{{\phi(y)}{p_{1}\left( {y - x} \right)}{\mathbb{d}y}}} \right){\mathbb{d}x}}}} \\ {= {{\int{{F_{1,\phi}(x)}{p_{n}(x)}{\mathbb{d}x}}} = {E_{n}\left( {F_{1,\phi}(x)} \right)}}} \end{matrix} & (11) \end{matrix}$

Similarly,

$\begin{matrix} {P_{FA}^{y} = {{\int_{R^{N}}{{p_{0}(x)}{C_{n,\phi}(x)}{\mathbb{d}x}}} = {E_{0}\left\lbrack {C_{n,\phi}(x)} \right\rbrack}}} & (12) \\ {\mspace{40mu}{{= {{\int{{F_{0,\phi}(x)}{p_{n}(x)}{\mathbb{d}x}}} = {E_{n}\left( {F_{0,\phi}(x)} \right)}}}{where}}} & (13) \\ {{{{F_{i,\phi}(x)} \equiv {\int_{R^{N}}{{\phi(y)}{p_{i}\left( {y - x} \right)}{\mathbb{d}y}\mspace{14mu} i}}} = 0},1,} & (14) \end{matrix}$ corresponding to hypothesis H_(i). E_(i)(•), E_(n)(•) are the expected values based on distributions p_(i) and p_(n), respectively, and P^(x) _(FA)=F_(0,φ)(0), P^(x) _(D)=F_(1,φ)(0). To simplify notation, subscript φ of F and C may be omitted and denotes as F₁, F₀, and C_(n), respectively. Further, F₁(x₀) and F₀(x₀) are actually the probability of detection and probability of false alarm, respectively, for this detection scheme with input y=x+x₀. For example, F₁(−2) is the P_(D) of this detection scheme with input x−2. Therefore, it is very convenient to obtain the F₁ and F₀ values by analytical computation if p₀, p₁ and φ are known. When they are not available, F₁ and F₀ can be obtained from the data itself by processing it through the detector and recording the detection performance. The optimal SR noise definition may be formalized as follows.

Consider the two hypotheses detection problem. The pdf of optimum SR noise is given by

$\begin{matrix} {{p_{n}^{opt} = {\underset{P_{n}}{\arg\;\max}{\int_{R^{N}}{{F_{1}(x)}{p_{n}(x)}{\mathbb{d}x}}}}}{where}} & (15) \\ {{{p_{n}(x)} \geq 0},{x \in {R^{N}.}}} & \left. 1 \right) \\ {{\int_{R^{N}}{{p_{n}(x)}{\mathbb{d}x}}} = 1.} & \left. 2 \right) \\ {{\int_{R^{N}}{{F_{0}(x)}{p_{n}(x)}{\mathbb{d}x}}} \leq {{F_{0}(0)}.}} & \left. 3 \right) \end{matrix}$

Conditions 1) and 2) are fundamental properties of a pdf function. Condition 3) ensures that P^(y) _(FA)≦P^(x) _(FA), i.e., the P_(FA) constraint specified under the Neyman-Pearson Criterion is satisfied. Further, if the inequality of condition 3) becomes equality, the Constant False Alarm Rate (CFAR) property of the original detector is maintained. A simple illustration of the effect of additive noise is shown in FIG. 1. In the example,

${{F_{1}\left( {- A} \right)} = {{\max\limits_{x}{{F_{1}(x)}\mspace{11mu}{and}\mspace{11mu}{F_{0}\left( {- A} \right)}}} < {F_{0}(0)}}},$ hence p _(n) ^(opt)=δ(x+A) which means the optimal SR noise n=−A is a dc signal with value −A. In practical applications, some additional restrictions on the noise may also be applied. For example, the type of noise may be restricted, (e.g., n may be specified as Gaussian noise), or we may require a noise with even symmetric pdf p_(n)(x)=p_(n)(−x) to ensure that the mean value of y is equal to the mean value of x. However, regardless of the additional restrictions, the conditions 1), 2), and 3) are always valid and the optimum noise pdf can be determined for these conditions.

In general, for optimum SR noise detection in Neyman-Peason detection, it is difficult to find the exact form of p_(n)(•) directly because of condition 3). However, an alternative approach considers the relationship between p_(n)(x) and F_(i)(x). From equation (14), for a given value f₀ of F₀, we have x=F₀ ⁻¹(f₀), where F₀ ⁻¹ is the inverse function of F₀. When F₀ is a one-to-one mapping function, x is a unique vector. Otherwise, F₀ ⁻¹(f₀) is a set of x for which F₀(x)=f₀. Therefore, we can express a value or a set of values f₁ of F₁ as ƒ₁ =F ₁(x)=F ₁(F ₀ ⁻¹(ƒ₀))  (16)

Given the noise distribution of p_(n)(•) in the original RN domain, p_(n,ƒ) ₀ (•), the noise distribution in the f₀ domain can also be uniquely determined. Further, the conditions on the optimum noise can be rewritten in terms of f₀ equivalently as

$\begin{matrix} \begin{matrix} \left. 4 \right) & {{p_{n,f_{0}}\left( f_{0} \right)} \geq 0} \end{matrix} & \; \\ \begin{matrix} \left. 5 \right) & {{\int{{p_{n,f_{0}}\left( f_{0} \right)}{\mathbb{d}f_{0}}}} = 1} \end{matrix} & \; \\ \begin{matrix} \left. 6 \right) & {{\int{f_{0}{p_{n,f_{0}}\left( f_{0} \right)}{\mathbb{d}f_{0}}}} \leq P_{FA}^{x}} \end{matrix} & \; \\ {and} & \; \\ {{P_{D}^{y} = {\int_{0}^{1}{f_{1}{p_{n,f_{0}}\left( f_{0} \right)}{\mathbb{d}f_{0}}}}},} & (17) \end{matrix}$ where p_(n,ƒ) ₀ (•) is the SR noise pdf in the f₀ domain.

Compared to the original conditions 1), 2) and 3), this equivalent form has some advantages. First, the problem complexity is dramatically reduced. Instead of searching for an optimal solution in R^(N), the present invention seeks an optimal solution in a single dimensional space. Second, by applying these new conditions, the present invention avoids the direct use of the underlying pdfs p₁(•) and p₀(•) and replace them with f₁ and f₀. Note that, in some cases, it is not very easy to find the exact form of f₀ and f₁. However, recall that F₁(x₀) and F₀(x₀) are the Probability of Detection and Probability of False Alarm, respectively, of the original system with input x+x₀. In practical applications, the relationship may be determined by Monte Carlo simulation using importance sampling. In general, compared to p₁ and p₀, f₁ and f₀ are much easier to estimate and once the optimum p_(n,ƒ) ₀ is found, the optimum p_(n)(x) is determined as well by the inverse of the functions F₀ and F₁.

Consider the function J(t), such that J(t)=sup(f₁:f₀=t) is the maximum value of f₁ given f₀. Clearly, J(P^(x) _(FA))≧F₁(0)=P^(x) _(D). It follows that for any noise p_(n), P _(D) ^(y)(p _(n))=∫₀ ¹ J(f ₀)p _(n,ƒ) ₀ (ƒ₀)dƒ ₀  (18) Therefore, the optimum P_(D) ^(y) is attained when f₁(f₀)=J(f₀) and P_(D,opt) ^(y)=E_(n)(J).

Improvability of a given detector when SR noise is added can be determined by computing and comparing P_(D,opt) ^(y) and P_(D) ^(x). When P_(D,opt) ^(y)>P_(D) ^(x), the given detector is improvable by adding SR noise. However, it requires the complete knowledge of F₁(•) and F₀(•) and significant computation. For a large class of detectors, however, depending on the specific properties of J, it is possible to determine the sufficient conditions for improvability and non-improvability more easily. The conditions are determined using the following theorems.

Theorem 1 (Improvability of Detection via SR): If J(P_(FA) ^(x))>P_(D) ^(x) or J″(P_(FA) ^(x))>0 when J(t) is second order continuously differentiable around P_(FA) ^(x), then there exists at least one noise process n with pdf p_(n)(•) that can improve the detection performance.

Proof: First, when J(P_(FA) ^(x))>P_(D) ^(x), from the definition of J function, we know that there exists at one least one n₀ such that F₀(n₀)=P_(FA) ^(x) and F₁(n₀)=J(P_(FA) ^(x))>P_(D) ^(x). Therefore, the detection performance can be improved by choosing a SR noise pdf P_(n)(n)=δ(n−n₀). When J″(P_(FA) ^(x))>0 and is continuous around P_(FA) ^(x), there exists an ε>0 such that J″(•)>0 on I=(P_(FA) ^(x)−ε, P_(FA) ^(x)+ε). Therefore, from Theorem A-1, J is convex on I. Next, add noise n with pdf p_(n)(x)=½δ(x−x₀)+½δ(x+x₀), where F₀(x₀)=P_(FA) ^(x)+ε/2 and F₀(x₁)=P_(FA) ^(x)−ε/2. Due to the convexity of J,

$P_{D}^{y} = {\frac{{J\left( {P_{FA}^{x} - \frac{ɛ}{2}} \right)} + {J\left( {P_{FA}^{x} - \frac{ɛ}{2}} \right)}}{2} > {J\left( P_{FA}^{x} \right)} \geq P_{D}^{x}}$ Thus, detection performance can be improved via the addition of SR noise.

Theorem 2 (Non-improvability of Detection via SR): If there exists a non-decreasing concave function Ψ(f₀) where Ψ(P^(x) _(FA))=J(P^(x) _(FA))=F₁(0) and Ψ(f₀)≧J(f₀) for every f₀, then P_(D) ^(y)≦P_(D) ^(x) for any independent noise, i.e., the detection performance cannot be improved by adding noise.

Proof: For any noise n and corresponding y, we have

$\begin{matrix} \begin{matrix} {{P_{D}^{y}\left( p_{n} \right)} = {\int_{0}^{1}{{J\left( f_{0} \right)}{p_{n,f_{0}}\left( f_{0} \right)}{\mathbb{d}f_{0}}}}} \\ {\leq {\int_{0}^{1}{{\Psi\left( f_{0} \right)}{p_{n,f_{0}}\left( f_{0} \right)}{\mathbb{d}f_{0}}}}} \\ {\leq {\Psi\left( {\int_{0}^{1}{f_{0}{p_{n,f_{0}}\left( f_{0} \right)}{\mathbb{d}f_{0}}}} \right)}} \\ {{\leq {\Psi\left( P_{FA}^{x} \right)}} = P_{D}^{x}} \end{matrix} & (19) \end{matrix}$ The third inequality of the Right Hand Side (RHS) of (19) is obtained using the concavity of the Ψ function. The detection performance cannot be improved via the addition of SR noise.

Before determining the form of the optimum SR noise PDF, i.e., the exact pdf of p_(n) ^(opt), the following result for the form of optimum SR noise must be determined.

Theorem 3 (Form of Optimum SR Noise): To maximize P_(D) ^(y), under the constraint that P_(FA) ^(y)≦P_(FA) ^(x), the optimum noise can be expressed as: p _(n) ^(opt)(n)=λδ(n−n ₁)+(1−λ)δ(n−n ₂)  (20) where 0≦λ≦1. In other words, to obtain the maximum achievable detection performance given the false alarm constraints, the optimum noise is a randomization of two discrete vectors added with the probability λ and 1−λ, respectively.

Proof: Let U={(f₁, f₀)|f₁=F₁(x), f₀=F₀(x), x

R^(N)) be the set of all pairs of (f₁; f₀). Since 0≦f₁; f₀≦1, U is a subset of the linear space R². Furthermore, let V be the convex hull of U. Since V⊂R², its dimension Dim(V)≦2. Similarly, let the set of all possible (P_(D) ^(y); P_(FA) ^(y)) be W. Since any convex combination of the elements of U, say

$\mspace{20mu}{\left( {\chi,\phi} \right) = {\sum\limits_{i = 1}^{M}{\alpha_{i}\left( {f_{1,i},f_{0,i}} \right)}}}$ can be obtained by setting the SR noise pdf such that

${p_{n,f_{0}}\left( f_{0} \right)} = {\sum\limits_{i = 1}^{M}{\alpha_{i}{\delta\left( {f_{0} - f_{0,i}} \right)}}}$ V⊂W. It can also be shown that W⊂V. Otherwise, there would exist at least one element z such that z

W, but z∉V. In this case, there exists a small set S and a positive number τ such that S={(x,y)|∥(x,y)−z∥₂ ²<τ} and S∩V=‘{ }’ where ‘{ }’ denotes an empty set. However, since 0≦f₁; f₀≦1, by the well known property of integration, there always exists a finite set E with finite elements such that E⊂U and (x₁; y₁), a convex combination of the elements of E, such that ∥(x ₁ ,y ₁)−z∥ ₂ ²<τ

Since (x₁; y₁)

V, then (x₁; y₁)

(V∩S) which contradicts the definition of S. Therefore, W⊂V. Hence, W=V. From Theorem A-4, (P^(y) _(D); P^(y) _(FA)) can be expressed as a convex combination of three elements. Also, since we are only interested in maximizing P_(D) under the constraint that P_(FA) ^(y)≦P_(FA) ^(x), the optimum pair can only belong to B, the set of the boundary elements of V. To show this, let (f₁*; f₀*) be an arbitrary non-boundary point inside V. Since there exists a τ>0 such that (f₁*+τ, f₀*)

V, then (f₁*; f₀*) is inadmissible as an optimum pair. Thus, the optimum pair can only exist on the boundary and each z on the boundary of V can be expressed as the convex combination of only two elements in U. Hence, (P _(D,opt) ^(y) ,P _(FA,opt) ^(y))=λ(ƒ₁₁,ƒ₀₁)+(1−λ)(ƒ₁₂,ƒ₀₂)  (21) where (f₁₁; f₀₁); (f₁₂; f₀₂)

U, 0≦λ≦1. Therefore, we have p _(n,ƒ) ₀ ^(opt)=λδ(ƒ₀−ƒ₀₁)+(1−λ)δ(ƒ₀−ƒ₀₂)  (22)

Equivalently, p_(n) ^(opt)(n)=λδ(n−n₁)+(1−λ)δ(n−n₂), where n₁ and n₂ are determined by the equations

$\begin{matrix} \left\{ \begin{matrix} {{F_{0}\left( n_{1} \right)} = f_{01}} \\ {{F_{1}\left( n_{1} \right)} = f_{11}} \\ {{F_{0}\left( n_{2} \right)} = f_{02}} \\ {{F_{1}\left( n_{2} \right)} = f_{12}} \end{matrix} \right. & (23) \end{matrix}$

Alternatively, the optimum SR noise can also be expressed in terms of C_(n), such that C _(n) ^(opt)(x)=λφ(x+n ₁)+(1−λ)φ(x+n ₂)  (24)

From equation (22), we have P _(D,opt) ^(y) =λJ(ƒ₀₁)+(1−λ)J(ƒ₀₂)  (25) and P _(FA,opt) ^(y)=λƒ₀₁+(1−λ)ƒ₀₂ ≦P _(FA) ^(x)  (26)

Depending on the location of the maxima of J(•), determination of the pdf of optimum SR noise may be accomplished according to the following theorem.

Theorem  4: ${{Let}\mspace{14mu} F_{1M}} = {{{\max\left( {J(t)} \right)}\mspace{11mu}{and}\mspace{14mu} t_{0}} = {\arg\mspace{14mu}{\min\limits_{t}{\left( {{J(t)} = F_{1M}} \right).}}}}$ It follows that

Case 1: If t_(o)≦P_(FA) ^(x), then P_(FA, opt) ^(x)=t_(o) and P_(D, opt) ^(y)=F_(1M), i.e., the maximum achievable detection performance is obtained when the optimum noise is a DC signal with value no, i.e., p _(n) ^(opt)(n)=δ(n−n ₀)  (27) where F₀(n_(o))=t_(o) and F₁(n_(o))=F_(1M).

Case 2: If t_(o)>P_(FA) ^(x), then P_(FA,opt) ^(x)=F₀(0)=P_(FA) ^(x), i.e., the inequality of (26) becomes equality. Furthermore, P _(FA,opt) ^(y)=λƒ₀₁+(1−λ)ƒ₀₂ =P _(FA) ^(x)  (28)

Proof: For Case 1, notice that

P_(D)^(y) = ∫₀¹J(f₀)p_(n, f₀)(f₀)𝕕f₀ ≤ ∫₀¹F_(1M)p_(n, f₀)(f₀)𝕕f₀ = F_(1M) and F₁(n₀)=F_(1M). Therefore the optimum detection performance is obtained when the noise is a DC signal with value n₀ with P_(FA) ^(y)=t₀.

The contradiction method is used to prove Case 2. First, supposing that the optimum detection performance is obtained when P_(FA,opt) ^(y)=κ<P_(FA) ^(x) with noise pdf p_(n,ƒ) ₀ ^(opt)(ƒ₀).

${{Let}\mspace{14mu}{p_{n_{1},f_{0}}\left( f_{0} \right)}} = {{\frac{P_{FA}^{x} - \kappa}{t_{0} - \kappa}{\delta\left( {f_{0} - t_{0}} \right)}} + {\frac{t_{0} - P_{FA}^{x}}{t_{0} - \kappa}{{p_{n,f_{0}}^{opt}\left( f_{0} \right)}.}}}$ It is easy to verify that p_(n,f) ₀ (f₀) is a valid pdf. Let y₁=x+n1. We now have

${P_{FA}^{y_{1}} = {{{\frac{P_{FA}^{x} - \kappa}{t_{0} - \kappa}t_{0}} + {\frac{t_{0} - P_{FA}^{x}}{t_{0} - \kappa}\kappa}} = P_{FA}^{x}}},{and}$ $P_{D}^{y_{1}} = {{{\frac{P_{FA}^{x} - \kappa}{t_{0} - \kappa}F_{1\; M}} + {\frac{t_{0} - P_{FA}^{x}}{t_{0} - \kappa}P_{D,{opt}}^{y}}} > P_{D,{opt}}^{y}}$

But this contradicts (15), the definition of p_(n) ^(opt). Therefore, P_(FA,opt) ^(y)=P_(FA) ^(x), i.e., the maximum achievable detection performance is obtained when the probability of false alarm remains the same for the SR noise modified observation y.

For Case 2 of Theorem 4, i.e., when t₀>P_(FA) ^(x), let us consider the following construction to derive the form of the optimum noise pdf. From Theorem 4, we have the condition that P_(FA,opt) ^(y)=F₀(0)=P_(FA) ^(x) is a constant. Define an auxiliary function G such that G(ƒ₀ ,k)=J(ƒ₀)−kƒ ₀,  (29) where k

R. We have P_(D) ^(y)=E_(n)(J)=E_(n)(G(f₀, k))+kE_(n)(f₀)=E_(n)(G(f₀; k))+k P_(FA) ^(x). Hence, p_(n,ƒ) ₀ ^(opt) also maximizes E_(n)(G(f₀; k)) and vice versa. Therefore, under the condition that P_(FA) ^(y)=P_(FA) ^(x), maximization of P_(D) ^(y) is equivalent to maximization of E_(n)(G(f₀; k)). Divide the domain of f₀ into two intervals I₁=[0, P_(FA) ^(x)] and I₂=[P_(FA) ^(x), 1]. Let f₀₁(k) be the minimum value that maximizes G(f₀; k) in I₁ and let f₀₂(k) be the minimum value that maximizes G(f₀; k) in I₂. Also, let v₁(k)=G(f₀₁; k) and v₂(k)=G(f₀₂; k) be the corresponding maximum values. Since for any f₀, G(f₀; k) is monotonically decreasing when k is increasing, v₁(k) and v₂(k) are monotonically decreasing while f₀₁(k) and f₀₂(k) are monotonically non-increasing when k is increasing. Since G(f₀; 0)=J, therefore v₂(0)=F_(1M)>v₁(0), furthermore, when k is very large, we have v₁(k)=J(0)>v₂(k) =J(P_(FA) ^(x))−kP_(FA) ^(x). Hence, there exists at least one k₀>0 such that v₁(k₀)=v₂(k₀)≡v. For illustration purposes, the plots of G(f₀; k) for the detection problem discussed below are shown in FIG. 4. Divide the [0,1] interval into two non-overlapping parts A, {f₀₁(k₀), f₀₂(k₀)}, such that {f₀₁(k₀); f₀₂(k₀)}∪A=[0,1] and {f₀₁(k₀); f₀₂(k₀)}∩A={ }. Next, represent p_(n, f) ₀ (f₀) as p _(n,f) ₀ (ƒ₀)=α₁δ(ƒ₀−ƒ₀₁(k ₀))+α₂δ(ƒ₀−ƒ₀₂(k ₀))+I _(A)(ƒ₀)p _(n,ƒ) ₀ (ƒ₀)  (30) where I_(A)(f₀)=1 for f₀

A and is zero otherwise (an indicator function). From equation (5), we must have

$\begin{matrix} {{{\alpha_{1} + \alpha_{2} + {\int_{A}{p_{n,f_{0}}{\mathbb{d}f_{0}}}}} = 1},{and}} & (31) \\ \begin{matrix} {{E_{n}(G)} = {{\left( {\alpha_{1} + \alpha_{2}} \right)v} + {\int_{A}{{G\left( {f_{0},k_{0}} \right)}p_{n,f_{0}}{\mathbb{d}f_{0}}}}}} \\ {= {{v + {\int_{A}{\underset{\underset{\leq 0}{︸}}{\left( {{G\left( {f_{0},k_{0}} \right)} - v} \right)}p_{n,f_{0}}{\mathbb{d}f_{0}}}}} \leq v}} \end{matrix} & (32) \end{matrix}$

Note that J(f₀)≦v for all f₀

A. Clearly, the upper bound can be attained when p_(n,ƒ) ₀ =0 for all f₀

A, i.e., α₁+α₂=1. Therefore, P_(D,opt) ^(y) P_(D,opt) ^(y)=E_(n)(G)+k₀P_(FA) ^(x)=v+k₀P_(FA) ^(x). From equation (28), we have

$\begin{matrix} {{p_{n,f_{0}}^{opt}\left( f_{0} \right)} = {{\frac{{f_{02}\left( k_{0} \right)} - P_{FA}^{x}}{{f_{02}\left( k_{0} \right)} - {f_{01}\left( k_{0} \right)}}{\delta\left( {f_{0} - {f_{01}\left( k_{0} \right)}} \right)}} + {\frac{P_{FA}^{x} - {f_{01}\left( k_{0} \right)}}{{f_{02}\left( k_{0} \right)} - {f_{01}\left( k_{0} \right)}}{\delta\left( {f_{0} - {f_{02}\left( k_{0} \right)}} \right)}}}} & (33) \end{matrix}$

Notice that by letting

${\lambda = \frac{{f_{02}\left( k_{0} \right)} - P_{FA}^{x}}{{f_{02}\left( k_{0} \right)} - {f_{01}\left( k_{0} \right)}}},$ (33) is equivalent to (22).

Equivalently, we have the expression of p_(n) ^(opt)(n) as

$\begin{matrix} {{p_{n}^{opt}(n)} = {{\frac{{f_{02}\left( k_{0} \right)} - P_{FA}^{x}}{{f_{02}\left( k_{0} \right)} - {f_{01}\left( k_{0} \right)}}{\delta\left( {n - n_{1}} \right)}} + {\frac{P_{FA}^{x} - {f_{01}\left( k_{0} \right)}}{{f_{02}\left( k_{0} \right)} - {f_{01}\left( k_{0} \right)}}{\delta\left( {n - n_{2}} \right)}}}} & (34) \end{matrix}$

Further, in the special case where f₁ is continuously differentiable, G is also continuously differentiable. Since f₀₁ and f₀₂ are at least local maxima, we have

${\frac{\partial G}{\partial f_{0}}\left( {f_{01},k_{0}} \right)} = {{\frac{\partial G}{\partial f_{0}}\left( {f_{02},k_{0}} \right)} = 0}$

Therefore, from the derivative of (29), we have

$\begin{matrix} {{{\frac{\mathbb{d}J}{\mathbb{d}f_{0}}\left( {f_{01}\left( k_{0} \right)} \right)} = {{\frac{\mathbb{d}J}{\mathbb{d}f_{0}}\left( {f_{02}\left( k_{0} \right)} \right)} = k_{0}}}{{{J\left( {f_{02}\left( k_{0} \right)} \right)} - {J\left( {f_{01}\left( k_{0} \right)} \right)}} = {k_{0}\left( {{f_{02}\left( k_{0} \right)} - \left( {f_{01}\left( k_{0} \right)} \right)} \right.}}} & {(35)\mspace{14mu}{and}\mspace{14mu}(36)} \end{matrix}$

In other words, the line connecting (J(f₀₁(k₀)),f₀₁(k₀)) and J(f₀₂(k₀)),f₀₂(k₀)) is the bi-tangent line of J(•) and k₀ is its slope. Also, P _(D,opt) ^(y) =v+k ₀ P _(FA) ^(x)  (37)

Thus, the condition under which SR noise can improve detection performance has been derived, and the specific form of the optimum SR noise has been obtained.

Detection Example

In a detection problem where two hypotheses H0 and H1 are given as

$\begin{matrix} \left\{ \begin{matrix} {{H_{0}{x\lbrack i\rbrack}} = {\omega\lbrack i\rbrack}} \\ {{{H_{1}{x\lbrack i\rbrack}} = {A + {\omega\lbrack i\rbrack}}},} \end{matrix} \right. & (38) \end{matrix}$ for i=0, 1, . . . , N−1, A>0 is a known dc signal, and w[i] are i.i.d noise samples with a symmetric Gaussian mixture noise pdf as follows

$\begin{matrix} {{{p_{w}(w)} = {{\frac{1}{2}{\gamma\left( {{w;{- \mu}},\sigma_{0}^{2}} \right)}} + {\frac{1}{2}{\gamma\left( {{w;\mu},\sigma_{0}^{2}} \right)}}}}{{and}\mspace{14mu}{where}}{{\gamma\left( {{w;\mu},\sigma_{0}^{2}} \right)} = {\frac{1}{\sqrt{2{\pi\sigma}^{2}}}{\exp\left\lbrack {- \frac{\left( {w - \mu} \right)^{2}}{2\sigma^{2}}} \right\rbrack}}}} & (39) \end{matrix}$ setting μ=3, A=1 and σ₀=1. A suboptimal detector is considered with test statistic

$\begin{matrix} {{T(x)} = {\frac{1}{N}{\sum\limits_{i = 0}^{N - 1}\left( {{\frac{1}{2} + {\frac{1}{2}{{sgn}\left( {x\lbrack i\rbrack} \right)}}} = {{\frac{1}{N}{\sum\limits_{i = 0}^{N - 1}{\left( {\varpi_{x}\lbrack i\rbrack} \right){where}{\varpi_{x}\lbrack i\rbrack}}}} = {\frac{1}{2} + {\frac{1}{2}{{{sgn}\left( {x\lbrack i\rbrack} \right)}.}}}}} \right.}}} & (40) \end{matrix}$

From equation (40), this detector is essentially a fusion of the decision results of N i.i.d. sign detectors.

When N=1, the detection problem reduces to a problem with the test statistic T₁(x)=x, thresholds η=0 (sign detector) and the probability of false alarm P_(FA) ^(x)=0.5. The distribution of x under the H₀ and H₁ hypotheses can be expressed as

$\begin{matrix} {{{p_{0}(x)} = {{\frac{1}{2}{\gamma\left( {{x;{- \mu}},\sigma_{0}^{2}} \right)}} + {\frac{1}{2}{\gamma\left( {{x;\mu},\sigma_{0}^{2}} \right)}}}}{and}} & (41) \\ {{p_{1}(x)} = {{\frac{1}{2}{\gamma\left( {{x;{{- \mu} + A}},\sigma_{0}^{2}} \right)}} + {\frac{1}{2}{\gamma\left( {{x;{\mu + A}},\sigma_{0}^{2}} \right)}}}} & (42) \end{matrix}$ respectively. The critical function is given by

$\begin{matrix} {{\phi(x)} = \left\{ \begin{matrix} 1 & {x > 0} \\ 0 & {x \leq 0.} \end{matrix} \right.} & (43) \end{matrix}$

The problem of determining the optimal SR noise is to find the optimal p(n) where for the new observation y=x+n, the probability of detection P_(D) ^(y)=p(y>0;H₁) is maximum while the probability of false alarm P_(FA) ^(y)=p(y>0;H₀)≦P_(FA) ^(x)=½.

When N>1, the detector is equivalent to a fusion of N individual detectors and the detection performance monotonically increases with N. Like the N=1 case, when the decision function is fixed, the optimum SR noise can be obtained by a similar procedure. Due to space limitations, only the suboptimal case where the additive noise n is assumed to be an i.i.d noise is considered here. Under this constraint, since the P_(D) _(S) and P_(FA) _(S) of each detector are the same, it can be shown that the optimal noise for the case N>1 is the same as N=1 because P_(FA)≦0.5 is fixed for each individual detector while increasing its P_(D). Hence, only the one sample case (N=1) is considered below. However, the performance of the N>1 case can be derived similarly.

The determination of the optimal SR noise pdf follows from equations (11) and (13), where it can be shown that in this case,

$\begin{matrix} {\begin{matrix} {{F_{1}(x)} = {\int_{0}^{+ \infty}{{\phi(y)}{p_{1}\left( {y - x} \right)}{\mathbb{d}y}}}} \\ {= {\frac{1}{2}\left( {\int_{0}^{+ \infty}{\left\lbrack {{\gamma\left( {{{y - x};{{- \mu} + A}},\sigma_{0}^{2}} \right)} + {\gamma\left( {{{y - x};{\mu + A}},\sigma_{0}^{2}} \right)}} \right\rbrack{\mathbb{d}y}}} \right)}} \\ {= {{\frac{1}{2}{Q\left( \frac{{- x} - \mu - A}{\sigma_{0}} \right)}} + {\frac{1}{2}{Q\left( \frac{{- x} + \mu - A}{\sigma_{0}} \right)}}}} \end{matrix}{and}} & (44) \\ {\begin{matrix} {{F_{0}(x)} = {\int_{0}^{\infty}{{\phi(y)}{p_{0}\left( {y - x} \right)}{\mathbb{d}y}}}} \\ {= {\frac{1}{2}\left( {\int_{0}^{+ \infty}{\left\lbrack {{\gamma\left( {{{y - x};{- \mu}},\sigma_{0}^{2}} \right)} + {\gamma\left( {{{y - x};\mu},\sigma_{0}^{2}} \right)}} \right\rbrack{\mathbb{d}y}}} \right)}} \\ {= {{\frac{1}{2}{Q\left( \frac{{- x} - \mu}{\sigma_{0}} \right)}} + {\frac{1}{2}{Q\left( \frac{{- x} + \mu}{\sigma_{0}} \right)}}}} \end{matrix}{where}{{Q(x)} = {\int_{x}^{\infty}{\frac{1}{\sqrt{2\pi}}{\exp\left( {{- t^{2}}/2} \right)}{{\mathbb{d}t}.}}}}} & (45) \end{matrix}$

It is also easy to show that, in this case, F₁(x)>F₀(x) and both are monotonically increasing with x. Therefore, J(f₀)=f₁(f₀)=f₁, and U=(f₁; f0) is a single curve. FIG. 2 shows the values of f₁ and f₀ as a function of x, while the relationship between F₁ and F₀ is shown in FIG. 3. V, the convex hull of all possible P_(D) and P_(FA) after n is added and shown as the light and dark shadowed regions in FIG. 3. Note that a similar non-concave ROC occurs in distributed detection systems and dependent randomization is employed to improve system performance. Taking the derivative of f₁ w.r.t. f₀, we have

$\begin{matrix} {{\frac{\mathbb{d}\left( f_{1} \right)}{\mathbb{d}\left( f_{0} \right)} = {\frac{\frac{\mathbb{d}\left( f_{1} \right)}{\mathbb{d}(x)}}{\frac{\mathbb{d}\left( f_{0} \right)}{\mathbb{d}(x)}} = \frac{p_{1}\left( {- x} \right)}{p_{0}\left( {- x} \right)}}},{and}} & (46) \\ \begin{matrix} {\frac{\mathbb{d}^{2}\left( f_{1} \right)}{\mathbb{d}\left( f_{0}^{2} \right)} = {\frac{1}{p_{0}\left( {- x} \right)}\frac{\mathbb{d}\left( \frac{p_{1}\left( {- x} \right)}{p_{0}\left( {- x} \right)} \right)}{\mathbb{d}x}}} \\ {= \frac{{{- {p_{1}^{\prime}\left( {- x} \right)}}{p_{0}\left( {- x} \right)}} + {{p_{0}^{\prime}\left( {- x} \right)}{p_{1}\left( {- x} \right)}}}{p_{0}^{3}\left( {- x} \right)}} \end{matrix} & (47) \end{matrix}$ where x=F₀ ⁻¹(f₀). Since

$\frac{\mathbb{d}{\gamma\left( {{{y - x};\mu},\sigma^{2}} \right)}}{\mathbb{d}x} = {\frac{\mu - x}{\sigma^{2}}{\gamma\left( {{{y - x};\mu},\sigma^{2}} \right)}}$ we have p′₀(−x)|_(x=0) and

$\begin{matrix} \begin{matrix} {\left. \frac{\mathbb{d}^{2}\left( f_{1} \right)}{\mathbb{d}\left( f_{0}^{2} \right)} \right|_{f_{0} = {f_{0}{(0)}}} = \left. \frac{{{- {p_{1}^{\prime}\left( {- x} \right)}}{p_{0}\left( {- x} \right)}} + {{p_{0}^{\prime}\left( {- x} \right)}{p_{1}\left( {- x} \right)}}}{p_{0}^{3}\left( {- x} \right)} \right|_{x = 0}} \\ {= \left. \frac{- {p_{1}^{\prime}\left( {- x} \right)}}{p_{0}^{2}\left( {- x} \right)} \right|_{x = 0}} \\ {= \frac{\left( {\mu - A} \right){\exp\left( {- \frac{\left( {\mu - A} \right)^{2}}{2\sigma_{0}^{2}}} \right)}}{\sqrt{2\pi}\sigma_{0}^{3}{p_{0}^{2}(0)}}} \\ {= {\frac{\left( {\mu + A} \right){\exp\left( {- \frac{\left( {\mu + A} \right)^{2}}{2\sigma_{0}^{2}}} \right)}}{\sqrt{2\pi}\sigma_{0}^{3}{p_{0}^{2}(0)}}.}} \end{matrix} & (48) \end{matrix}$

With respect to the improvability of this detector, when A<μ, setting (48) equal to zero and solving the equation for σ₀, we have σ₁, the zero pole of (48)

$\sigma_{1} = {\sqrt{2\frac{\mu\; A}{\ln\left( \frac{\mu + A}{\mu - A} \right)}}.}$

When σ₀<σ₁, then

$\left. \frac{\mathbb{d}^{2}\left( f_{1} \right)}{\mathbb{d}\left( f_{0}^{2} \right)} \middle| {}_{f_{0} = {F_{0}{(0)}}}{> 0} \right.$ and, in this example, σ₁ ²=8.6562>σ₀ ²=1. From Theorem 1, this detector is improvable by adding independent SR noise. When

${A > \mu},\left. \frac{\mathbb{d}^{2}\left( f_{1} \right)}{\mathbb{d}\left( f_{0}^{2} \right)} \middle| {}_{f_{0} = {f_{0}{(0)}}}{< 0} \right.$ the improvability cannot be determined by Theorem 1. However, for this particular detector, as discussed below, the detection performance can still be improved.

The two discrete values as well as the probability of their occurrence may be determined by solving equations (35) and (36). From equations (44) and (45), the relationship between f₁, f₀ and x, and equation (46), we have (49)

$\begin{matrix} \begin{matrix} {\frac{p_{1}\left( {- n_{1}} \right)}{p_{0}\left( {- n_{1}} \right)} = \frac{p_{1}\left( {- n_{2}} \right)}{p_{0}\left( {- n_{2}} \right)}} \\ {\frac{{F_{1}\left( n_{1} \right)} - {F_{1}\left( n_{2} \right)}}{{F_{0}\left( n_{1} \right)} - {F_{0}\left( n_{2} \right)}} = \frac{p_{1}\left( {- n_{2}} \right)}{p_{0}\left( {- n_{2}} \right)}} \end{matrix} & (49) \end{matrix}$

Although it is generally very difficult to solve the above equation analytically, in this particular detection problem,

${{p_{1}\left( {- \left( {\mu - \frac{A}{2}} \right)} \right)} = {{0.5{\gamma\left( {{{- \frac{A}{2}};0},\sigma_{0}^{2}} \right)}} + {0.5{\gamma\left( {{{{2\mu} + \frac{A}{2}};0},\sigma_{0}^{2}} \right)}}}},{{p_{0}\left( {- \left( {\mu - \frac{A}{2}} \right)} \right)} = {{0.5{\gamma\left( {{{- \frac{A}{2}};0},\sigma_{0}^{2}} \right)}} + {0.5{\gamma\left( {{{{2\mu} - \frac{A}{2}};0},\sigma_{0}^{2}} \right)}}}},{{p_{1}\left( {- \left( {\mu - \frac{A}{2}} \right)} \right)} = {{0.5{\gamma\left( {{{- \frac{A}{2}};0},\sigma_{0}^{2}} \right)}} + {0.5{\gamma\left( {{{{2\mu} - \frac{A}{2}};0},\sigma_{0}^{2}} \right)}}}},{{p_{0}\left( {- \left( {\mu - \frac{A}{2}} \right)} \right)} = {{0.5{\gamma\left( {{{- \frac{A}{2}};0},\sigma_{0}^{2}} \right)}} + {0.5{\gamma\left( {{{{2\mu} + \frac{A}{2}};0},\sigma_{0}^{2}} \right)}}}},{{so}\mspace{14mu}{that}}$ ${\frac{p_{1}\left( {- \left( {\mu - \frac{A}{2}} \right)} \right)}{p_{0}\left( {- \left( {\mu - \frac{A}{2}} \right)} \right)} \cong 1},{\frac{p_{1}\left( {- \left( {{- \mu} - \frac{A}{2}} \right)} \right)}{p_{0}\left( {- \left( {{- \mu} - \frac{A}{2}} \right)} \right)} \cong 1}$ and ${{F_{1}\left( \left( {\mu - \frac{A}{2}} \right) \right)} - {F_{1}\left( \left( {{- \mu} - \frac{A}{2}} \right) \right)}} = {{F_{0}\left( \left( {\mu - \frac{A}{2}} \right) \right)} - {F_{0}\left( \left( {{- \mu} - \frac{A}{2}} \right) \right)}}$ given 2μ−A/2>3σ₀. Thus, the roots n₁; n₂ of equation (49) can be approximately expressed as n₁=−μ−A/2 and n₂=μ−A/2.

Correspondingly,

$\begin{matrix} {\lambda = {{{\frac{{F_{0}\left( n_{2} \right)} - {F_{0}(0)}}{{F_{0}\left( n_{2} \right)} - {F_{0}\left( n_{1} \right)}}\mspace{14mu}{and}\mspace{14mu} 1} - \lambda} = {\frac{{F_{0}(0)} - {F_{0}\left( n_{1} \right)}}{{F_{0}\left( n_{2} \right)} - {F_{0}\left( n_{1} \right)}}.{Hence}}}} & \; \\ {\begin{matrix} {{p_{n}^{opt}(n)} = {{{\lambda\delta}\left( {n - n_{1}} \right)} + {\left( {1 - \lambda} \right){\delta\left( {n - n_{2}} \right)}}}} \\ {{= {{0.3085{\delta\left( {n + 3.5} \right)}} + {0.6915{\delta\left( {n - 2.5} \right)}}}},} \end{matrix}{and}} & (50) \\ {P_{D,{opt}}^{y} = {{{\lambda\;{F_{1}\left( n_{1} \right)}} + {\left( {1 - \lambda} \right){F_{1}\left( n_{2} \right)}}} = {0.6915.}}} & (51) \end{matrix}$

The present invention also encompasses special cases where the SR noise is constrained to be symmetric. These include symmetric noise with arbitrary pdf p_(s)(x), white Gaussian noise p_(g)(x)=γ(x; 0, σ²) and white uniform noise p_(u)(x)=1/a, a>0, −a/2≦x≦a/2. The noise modified data processes are denoted as y_(s), y_(g) and y_(u), respectively. Here, for illustration purposes, the pdfs of these suboptimal SR noises may be found by using the C(x) functions. The same results can be obtained by applying the same approach as in the previous subsection using F₁(•) and F₀(•) functions. For the arbitrary symmetrical noise case, we have the condition p _(s)(x)=p _(s)(−x).  (52)

Therefore, p(y|H₀) is also a symmetric function, so that P_(FA) ^(y) ^(s) =½. By equations (43) and (52), we have

$\begin{matrix} \begin{matrix} {{C_{s}(x)} = {\int_{0}^{\infty}{{p_{s}\left( {t - x} \right)}{\mathbb{d}t}}}} \\ {= {\int_{- x}^{\infty}{{p_{s}(t)}{\mathbb{d}t}}}} \\ {= {\int_{- \infty}^{x}{{p_{s}(t)}{\mathbb{d}t}}}} \\ {= {1 - {{C_{s}\left( {- x} \right)}.}}} \end{matrix} & (53) \end{matrix}$

Since p_(s)(x)≧0, we also have C _(s)(x ₁)≧C _(s)(x ₀) for any x₁≧x₀,  (54) and C _(s)(0)=½, C _(s)(−∞)=0, and C _(s)(∞) =1  (55)

From equations (9) and (53), we have the P_(D) of y_(s), given by

$\begin{matrix} \begin{matrix} {P_{D}^{y_{s}} = {\int_{- \infty}^{\infty}{{p_{1}(x)}{C_{s}(x)}{\mathbb{d}x}}}} \\ {= {{\int_{- \infty}^{0}{{p_{1}(x)}{C_{s}(x)}{\mathbb{d}x}}} + {\int_{0}^{\infty}{\left( {1 - {C_{s}\left( {- x} \right)}} \right){p_{1}(x)}{\mathbb{d}x}}}}} \\ {= {{\int_{- \infty}^{0}{\left( {{p_{1}(x)} - {p_{1}\left( {- x} \right)}} \right){C_{s}(x)}{\mathbb{d}x}}} + P_{D}^{x}}} \\ {{= {{\int_{- \infty}^{0}{{H(x)}{C_{s}(x)}{\mathbb{d}x}}} + P_{D}^{x}}},} \end{matrix} & (56) \end{matrix}$ where H(x)≡p₁(x)−p₁(−x). FIG. 5 shows a plot of H(x) for several σ₀ values. Finally, from equation (42), we have

${p_{1}\left( {- x} \right)} = {{\frac{1}{2}{\gamma\left( {{x;{\mu - A}},\sigma_{0}^{2}} \right)}} + {\frac{1}{2}{{\gamma\left( {{x;{{- \mu} - A}},\sigma_{0}^{2}} \right)}.}}}$

When A≧μ, since p₁(−x)≧p₁(x) when x<0, we have, H(x)<0, x<0. From equation (56), P_(D) ^(y)≧P_(D) ^(x) for any H(x), i.e., in this case, the detection performance of this detector cannot be improved by adding symmetric noise. When A<μ and σ₀≧σ₁, then H(x)<0, ∀x<0. Therefore, adding symmetric noise will not improve the detection performance as well. However, when σ₀≦σ₁, H(x) has only a single root x₀ for x<0 and H(x)<0, ∀x<x₀, H(x)>0, ∀x

(x₀, 0) and detection performance can be improved by adding symmetric SR noise. From equation (56), we have

$\begin{matrix} {{C_{s}^{opt}(x)} = \left\{ {{\begin{matrix} {0,} & {x < x_{0}} \\ {\frac{1}{2},} & {{x_{0} \leq x \leq 0},} \end{matrix}{and}p_{s}^{opt}} = {{\frac{1}{2}{\delta\left( {x - x_{0}} \right)}} + {\frac{1}{2}{{\delta\left( {x + x_{0}} \right)}.}}}} \right.} & (57) \end{matrix}$

Furthermore, since γ(−μ; −μ−A, σ² ₀)=γ(−μ; −μ+A, σ² ₀) and γ(−μ; −μ+A, σ² ₀)≈0 given 2μ−A>>σ₀, we have x₀≈−μ. Therefore,

$\begin{matrix} {p_{s}^{opt} = {{\frac{1}{2}{\delta\left( {x - \mu} \right)}} + {\frac{1}{2}{{\delta\left( {x + \mu} \right)}.}}}} & (58) \end{matrix}$

The pdf of y for the H₁ hypothesis becomes

$\begin{matrix} {{p_{1,y_{s}}^{opt}(y)} = {{\frac{1}{2}{\gamma\left( {{y;A},\sigma_{0}^{2}} \right)}} + {\frac{1}{4}{\gamma\left( {{y;{{2\mu} + A}},\sigma_{0}^{2}} \right)}} + {\frac{1}{4}{{\gamma\left( {{y;{{{- 2}\mu} + A}},\sigma_{0}^{2}} \right)}.}}}} & (59) \end{matrix}$

Hence, when μ is large enough,

$P_{D,{opt}}^{y_{s}} = {{{\frac{1}{2}{Q\left( {- \frac{A}{\sigma_{0}}} \right)}} + \frac{1}{4}} = {0.6707.}}$

Note that, as σ₀ decreases P_(D,opt) ^(y) ^(s) increases, i.e., better detection performance can be achieved by adding the optimal symmetric noise.

Similarly, for the uniform noise case,

$\begin{matrix} {{C_{u}(x)} = {{\int_{- x}^{\infty}{{p_{u}(t)}{\mathbb{d}t}}} = \left\{ \begin{matrix} {0,} & {x < \frac{- a}{2}} \\ {{\frac{x}{a} + \frac{1}{2}},} & {{- \frac{a}{2}} \leq x \leq 0} \end{matrix} \right.}} & (60) \end{matrix}$

Substituting equation (60) for C_(s)(x) in (56) and taking the derivative w.r.t α, we have

$\begin{matrix} {\frac{\mathbb{d}P_{D}^{y_{s}}}{\mathbb{d}\alpha} = {{- \frac{1}{\alpha^{2}}}{\int_{- \frac{\alpha}{2}}^{0}{x\;{H(x)}{{\mathbb{d}x}.}}}}} & (61) \end{matrix}$

Setting it equal to zero and solving, we have a_(opt)=8.4143 in the pdf of uniform noise defined earlier. Additionally, we have P_(D,opt) ^(y) ^(s) =0.6011.

For the Gaussian case, the optimal WGN level is readily determined since

$\begin{matrix} {P_{D}^{y_{s}} = {{\frac{1}{2}{Q\left( \frac{{- A} - \mu}{\sqrt{\sigma_{0}^{2} + \sigma^{2}}} \right)}} + {\frac{1}{2}{{Q\left( \frac{{- A} + \mu}{\sqrt{\sigma_{0}^{2} + \sigma^{2}}} \right)}.}}}} & (62) \end{matrix}$

Let σ² ₂=σ² ₀+σ² and take the derivative w.r.t σ₂ ² in equation (62), setting it equal to zero and solving, forming

$\begin{matrix} {{\sigma_{2}^{2} = {{2\frac{\mu\; A}{\ln\left( \frac{\mu + A}{\mu - A} \right)}} = 8.6562}},} & (63) \end{matrix}$ and σ² _(opt)=σ² ₂−σ² ₀=7.6562, and correspondingly, P_(D,opt) ^(y) ^(s) =0.5807. Therefore, when σ² ₀<σ² ₂, adding WGN with variance σ² _(opt) can improve the detection performance to a constant level P^(yg) _(D,opt).

Table 1 below is a comparison of detection performance for different SR noise enhanced detectors, and shows the values of P_(D, opt) ^(y) for the different types of SR noise. Compared to the original data process with P_(D) ^(x)=0.5114, the improvement of different detectors are 0.1811, 0.1593, 0.0897 and 0.0693 for optimum SR noise, optimum symmetric noise, optimum uniform noise and optimum Gaussian noise enhanced detectors, respectively.

SR Noise P_(n) ^(opt) P_(s) ^(opt) P_(u) ^(opt) P_(g) ^(opt) No SR Noise P_(D) ^(y) .6915 .6707 .6011 .5807 .5115

FIG. 6 shows P_(D) ^(x) as well as the maximum achievable P_(D) ^(y) with different values of A. The detection performance is significantly improved by adding optimal SR noise. When A≦μ, a certain degree of improvement is also observed by adding suboptimal SR noise. When A is small, x₀≈−μ and x₁≈μ, the detection performance of the optimum SR noise enhanced detector is close to the optimum symmetric noise enhanced one. However, when A>0.6, the difference is significant. When A>μ=3, H(x)<0; ∀x<0, so that P_(D,opt) ^(y) ^(s) =P_(D,opt) ^(y) ^(u) =P_(D, opt) ^(y) ^(g) =P_(D) ^(x), i.e, the optimal symmetric noise is zero (no SR noise). However, by adding optimal SR noise, P_(D,opt) ^(y) is still larger than P_(D) ^(x), i.e., the detection performance can still be improved. When A≧5, the P_(D) improvement is not that significant because P_(D) ^(x)>0.97≈1 which is already a very good detector.

The maximum achievable detection performance of different SR noise enhanced detectors with different background noise σ₀ is shown in FIG. 7. When σ₀ is small, for the optimum SR noise enhanced detectors P_(D,opt) ^(y)≈1, while for the symmetric SR noise case P_(D,opt) ^(y) ^(s) ≈0.75. When σ₀ increases, P_(D) ^(x) increases and the detection performance of SR noise enhanced detectors degrades. When σ₀≧σ₁, p₀(x) becomes a unimodal noise and the decision function φ is the same as the decision function decided by the optimum LRT test given the false alarm P_(FA)=0.5. Therefore, adding any SR noise will not improve P_(D). Hence, all the detection results converge to P_(D) ^(x).

FIG. 8 compares the detection performance of different detectors w.r.t. μ when A=1 and σ₀=1 is fixed. P_(D) ^(x), P_(D,opt) ^(y) ^(s) and P_(D,opt) ^(y) ^(g) monotonically decrease when μ increases. Also, there exists a unique μ value μ₀, such that when μ<μ₀ is small, p₀ is still a unimodal pdf, so that the decision function φ is the optimum one for P_(FA)=0.5. An interesting observation from FIG. 8 is that the P_(D) of the “optimum LRT” after the lowest value is reached, increases when μ increases. The explanation of this phenomenon is that when μ is sufficiently large, the separation of the two peaks of the Gaussian mixtures increases as μ increases so that the detectability is increased. When μ→∞, the two peaks are sufficiently separated, so that the detection performance of “LRT” is equal to the P_(D) when μ=0.

Finally, FIG. 9 shows the ROC curves for the detection problem when N=30 and the different types of i.i.d SR noise determined previously are added. Different degrees of improvement are observed for different SR noise pdfs. The optimum SR detector and the optimum symmetric SR detector performance levels are superior to those of the uniform and Gaussian SR detectors and more closely approximate the LRT curve. For LRT, the performance is nearly perfect (P_(D)≈1 for all P_(FA) _(s) ).

The present invention thus establishes a mathematical theory for the stochastic resonance (SR) noise modified detection problem, as well as several fundamental theorems on SR in detection theory. The detection performance of a SR noise enhanced detector is analyzed where, for any additive noise, the detection performance in terms of P_(D) and P_(FA) can be obtained by applying the expressions of the present invention. Based on these, the present invention established the conditions of potential improvement of P_(D) via the SR effect, which leads to the sufficient condition for the improvability/non-improvability of most suboptimal detectors.

The present invention also established the exact form of the optimal SR noise pdf. The optimal SR noise is shown to be a proper randomization of no more than two discrete signals. Also, the upper limit of the SR enhanced detection performance is obtained by the present invention. Given the distributions p₁ and p₀, the present invention provides an approach to determine the optimal SR consisting of the two discrete signals and their corresponding weights. It should be pointed out that the present invention is applicable to a variety of SR detectors, e.g., bistable systems.

The SR detectors that may be implemented with the present invention are shown in FIG. 10. For example, the nonlinear system block of FIG. 10 can depict the bistable system. Let x=[x₁, x₂, . . . , x_(n)]^(T) be the input to the nonlinear system, and x′=[x₀₁, x₀₂, . . . , x_(N)]^(T) be the output of the system as shown, where x′=f(x) is the appropriate nonlinear function. The decision problem based on x′ can be described by decision function φ₀(•) as shown. It is easy to observe that the corresponding decision function φ(•) for the ‘super’ detector (i.e., the nonlinear system plus detector) is φ(x)=φ₀(f(x)). Thus, the SR detectors can be viewed as the system in FIG. 10 without the additive SR noise n. To summarize, the present invention admits conventional SR systems and allows improved detection system by adding n as shown in FIG. 10.

FIG. 11 illustrates a diagram of a SR detection system obtained by a modification of the observed data, x. The statistical properties of the data are changed by adding independent SR noise n to yield a new process y such that y=x+n. This process, in turn is provided as input to the noise modified detector.

Based on the mathematical framework of the present invention, for a particular detection problem, the detection performance of six different detectors are compared, namely, the optimum LRT detector, optimum noise enhanced sign detector, optimum symmetric noise enhanced sign detector, optimum uniform noise enhanced sign detector, optimum Gaussian noise enhanced sign detector and the original sign detector. Compared to the traditional SR approach where the noise type is predetermined, much better detection performance is obtained by adding the proposed optimum SR noise to the observed data process. The present invention thus corresponds with the observed SR phenomenon in signal detection problems, and greatly advances the determination the applicability of SR in signal detection. The present invention can also be applied to many other signal processing problems such as distributed detection and fusion as well as pattern recognition applications.

The present invention may thus be used to increase the probability of detecting signals embedded in non-Gaussian noise. The first step is to record data from an observed data process. Next, stochastic resonance noise is added to said recorded. The appropriate stochastic resonance noise is controlled by determining the stochastic resonance noise probability density function (PDF) that does not increase the detector probability of false alarm.

The SR noise may be determined for the case of a known data probability density function by determining from the known probability density function of the observed data process the stochastic resonance noise PDF that equals λδ(n−n₁)+(1−λ)δ(n−n₂), with values n₁ and n₂ equal to those of the two delta function locations, and with probabilities equal to λ and (1−λ), respectively. More specifically, the stochastic resonance noise PDF may be calculated by determining F_(i)(x)=∫_(R) _(N) φ(y) p_(i)(y−x)dy i=0,1, using known critical function φ(y) and known data probability density functions p_(i)(•), i=0,1; determining the three unknown quantities n₁, n₂, and λ using the known values k₀, ƒ₀₁ and ƒ₀₂ and the following three equations:

$\begin{matrix} {{\frac{\mathbb{d}J}{\mathbb{d}f_{0}}\left( {f_{01}\left( k_{0} \right)} \right)} = {\frac{\mathbb{d}J}{\mathbb{d}f_{0}}\left( {{f_{02}\left( k_{0} \right)};} \right.}} & (i) \\ {{{\frac{\mathbb{d}J}{\mathbb{d}f_{0}}\left( {f_{02}\left( k_{0} \right)} \right)} = k_{0}};} & ({ii}) \\ {{J\left( {f_{02}\left( k_{0} \right)} \right)} - {J\left( {{{f_{01}\left( k_{0} \right)} = {k_{0}\left( {{f_{02}\left( k_{0} \right)} - {f_{01}\left( k_{0} \right)}} \right)}};} \right.}} & ({iii}) \end{matrix}$ and determining the probability of occurrence for n₁ and n₂ as λ and 1−λ, respectively, using the equation

$\lambda = {\frac{{f_{02}\left( k_{0} \right)} - P_{FA}^{x}}{{f_{02}\left( k_{0} \right)} - {f_{01}\left( k_{0} \right)}}.}$

Alternatively, the SR noise for the case of a known data probability density function may be calculated by determining the stochastic resonance noise PDF that consists of a single delta function, δ(n−n₀) with value no equal to the delta function location with probability one. The minimum probability of error may be calculated from

$P_{e,\min} = {\pi_{1}\left\lbrack {1 - {\max\limits_{f_{0}}{G\left( {f_{0},\frac{\pi_{0}}{\pi_{1}}} \right)}}} \right\rbrack}$ where G(ƒ₀, k)=J(ƒ₀)−kf₀=P_(D)−kP_(FA). The single delta function located at n₀ is calculated from n₀=F₀ ⁻¹(ƒ₀), where ƒ₀ is the value the maximizes

${G\left( {f_{0},\frac{\pi_{0}}{\pi_{1}}} \right)}.$

The SR noise for the case of labeled data with an unknown data PDF may be determined by first calculating the stochastic resonance noise PDF that consists of two delta functions. This step is accomplished by estimating the stochastic resonance noise consisting of two random variables n₁ and n₂ by using many algorithms, such as expectation-maximization (EM) and the Karzen method to estimate the unknown data PDFs, and applying the estimated PDFs and the stochastic resonance noise PDF may be calculated by determining F_(i)(x)=∫_(R) _(N) φ(y)p_(i)(y−x)dy i=0,1, using known critical function φ(y) and known data probability density functions p_(i)(•), i=0,1; determining the three unknown quantities n₁, n₂, and λ using the known value k₀, and estimated values {circumflex over (ƒ)}₀₁, {circumflex over (ƒ)}₀₂, and Ĵ in the following three equations:

$\begin{matrix} {{\frac{\mathbb{d}\hat{J}}{\mathbb{d}f_{0}}\left( {{\hat{f}}_{0}\left( k_{0} \right)} \right)} = {\frac{\mathbb{d}\hat{J}}{\mathbb{d}f_{0}}\left( {{{\hat{f}}_{02}\left( k_{0} \right)};} \right.}} & (i) \\ {{{\frac{\mathbb{d}\hat{J}}{\mathbb{d}f_{0}}\left( {{\hat{f}}_{02}\left( k_{0} \right)} \right)} = k_{0}};} & ({ii}) \\ {{\hat{J}\left( {{\hat{f}}_{02}\left( k_{0} \right)} \right)} - {\hat{J}\left( {{{{\hat{f}}_{01}\left( k_{0} \right)} = {k_{0}\left( {{{\hat{f}}_{02}\left( k_{0} \right)} - {{\hat{f}}_{01}\left( k_{0} \right)}} \right)}};} \right.}} & ({iii}) \end{matrix}$ and determining the probability of occurrence for n₁ and n₂ as λ and 1−λ, respectively, using the equation

$\lambda = {\frac{{{\hat{f}}_{02}\left( k_{0} \right)} - P_{FA}^{x}}{{{\hat{f}}_{02}\left( k_{0} \right)} - {{\hat{f}}_{01}\left( k_{0} \right)}}.}$

The next step is to determine the stochastic resonance noise consisting of two random variables n₁ and n₂ with values equal to those of the two delta function locations and with probabilities equal to those of said stochastic resonance noise probability density function; adding said stochastic resonance noise to said data; applying said fixed detector to the resulting data process.

Finally, a test statistic for signal detection is calculated under a constant probability of false alarm rate (CFAR) constraint, such that the performance of suboptimal, nonlinear, fixed detectors operating in said non-Gaussian noise are improved. Increasing the probability of detecting signals embedded in non-Gaussian noise comprises adding the stochastic resonance noise n₁ and n₂ with probability λ and 1−λ, respectively, to the data, and applying the fixed detector to the resulting data process.

The present invention also provides a method for evaluating functions using f₁, J(f₀), and dJ/df₀ where for any f₀, the equation x=F₀ ⁻¹(ƒ₀) is solved, and the value of f₁ is obtained by

$\begin{matrix} {{f_{1} = {F_{1}(x)}},{J\left( f_{0} \right)}} \\ {{= {\max\limits_{f_{1}}\left( {f_{1}\left( f_{0} \right)} \right)}},} \\ {{{and}\mspace{14mu}\frac{\mathbb{d}J}{\mathbb{d}f_{0}}} = {\lim\limits_{\Delta\rightarrow 0}{\frac{{J\left( {f_{0} + \Delta} \right)} - {J\left( f_{0} \right)}}{\Delta}.}}} \end{matrix}$

Following is background information relative to the formulas of the present invention and the applicable theorems on convex functions and convex sets.

A. Convex Functions

A function ƒ: I→R is called convex if ƒ(λx+(1−λ)y)≦λƒ(x)+(1−λ)ƒ(y)  (64) for all x,yεI and λ in the open interval (0,1). It is called strictly convex provided that the inequality (64) is strict for x≠y. Similarly, if −ƒ: I→R is convex, then we say that ƒ: I→R is concave.

Theorem A-1: Suppose ƒ″ exists on (a,b). Then ƒ is convex if and only if ƒ″(x)≧0. And if ƒ″(x)>0 on (a,b), then ƒ is strictly convex on the interval.

B. Convex Sets

Let U be a subset of a linear space L. We say that U is convex if x,yεU implies that z=[λx+(1−λ)y]εU for all λε[0,1].

Theorem A-2: A set U⊂L is convex if and only if every convex combination of points of U lies in U.

We call the intersection of all convex sets containing a given set U the convex hull of U denoted by H(U).

Theorem A-3: For any U⊂L, the convex hull of U consists precisely of all convex combinations of elements of U.

Furthermore, for the convex hull, we have Carathéodory's theorem for convex sets.

Theorem A-4 (Carathéodory's Theorem): If U⊂L, and its convex hull of H(U) has dimension m, then for each zεH(U), there exists m+1 points x₀, x₁, . . . x_(m) of U such that z is a convex combination of these points. 

1. A method of improving the detection of at least one signal embedded in non-Gaussian noise, said method comprising the steps of: (a) recording an observed data process; (b) calculating with a processor stochastic resonance noise by determining the stochastic resonance noise probability density function that does not increase the probability of false alarm and calculating the stochastic resonance noise data probability density function from a known probability density function for said observed data process, wherein the stochastic resonance noise probability density function equals λδ(n−n₁)+(1−λ)δ(n−n₂), with values n₁ and n₂ equal to two delta function locations having probabilities of λ and (1−λ), respectively; (c) adding said stochastic resonance noise to said data; and (d) displaying the results of adding said stochastic noise to said data, thereby improving the detection of said at least one signal in said non-Gaussian noise.
 2. The method of claim 1, wherein the step of calculating the stochastic resonance noise probability density function further comprises the steps of (i) determining F_(i)(x)=∫_(R) _(N) φ(y)p_(i)(y−x)dy i=0,1 using known critical function φ(y) and known data probability density functions p_(i)(•), i=0,1; (ii) determining the three unknown quantities n₁, n₂, and λ using the known values k₀, ƒ₀₁ and ƒ₀₂ using equations, $\begin{matrix} {{\frac{\mathbb{d}J}{\mathbb{d}f_{0}}\left( {f_{01}\left( k_{0} \right)} \right)} = {\frac{\mathbb{d}J}{\mathbb{d}f_{0}}\left( {{f_{02}\left( k_{0} \right)},} \right.}} \\ {{{\frac{\mathbb{d}J}{\mathbb{d}f_{0}}\left( {f_{02}\left( k_{0} \right)} \right)} = k_{0}},} \end{matrix}$ and J(θ₀₂(k₀))−J(ƒ₀₁(k₀)=k₀(ƒ₀₂(k₀)−ƒ₀₁(k₀)); and (iii) determining the probability of occurrence for n₁ and n₂ as λ and 1−λ, respectively, using the equation $\lambda = {\frac{{f_{02}\left( k_{0} \right)} - P_{FA}^{x}}{{f_{02}\left( k_{0} \right)} - {f_{01}\left( k_{0} \right)}}.}$
 3. The method of claim 2, wherein the step of adding said stochastic resonance noise to said data comprises the steps of: adding said stochastic resonance noise n₁ and n₂ with probability λ and 1−λ, respectively, to said data to form a data process; and applying a fixed detector to said data process.
 4. The method of claim 1, further comprising the steps of: determining from said observed data process the stochastic resonance noise probability density function that consists of two delta functions; determining stochastic resonance noise consisting of two random variables n₁ and n₂ with values equal to those of said two delta function locations and having probabilities equal to those of said stochastic resonance noise probability density function adding said stochastic resonance noise to said data; and applying a fixed detector to the resulting data process.
 5. The method of claim 4, wherein the step of determining from said observed data process the stochastic resonance noise probability density function that consists of two delta functions comprises the steps of: estimating the stochastic resonance noise consisting of two random variables n₁ and n₂ by estimating the unknown data probability density functions; applying estimated probability density functions; determining F_(i)(x)=∫_(R) _(N) φ(y)p_(i)(y−x)dy i=0,1, using known critical function φ(y) and known data probability density functions p_(i)(•), i=0,1; determining the three unknown quantities n₁, n₂, and λ using the known value k₀, and estimated values for {circumflex over (ƒ)}₀₁, {circumflex over (ƒ)}₀₂, and Ĵ in the equations $\begin{matrix} {{\frac{\mathbb{d}\hat{J}}{\mathbb{d}f_{0}}\left( {{\hat{f}}_{01}\left( k_{0} \right)} \right)} = {\frac{\mathbb{d}\hat{J}}{\mathbb{d}f_{0}}\left( {{{\hat{f}}_{02}\left( k_{0} \right)},} \right.}} \\ {{{\frac{\mathbb{d}\hat{J}}{\mathbb{d}f_{0}}\left( {{\hat{f}}_{02}\left( k_{0} \right)} \right)} = k_{0}},} \end{matrix}$ and Ĵ({circumflex over (ƒ)}₀₂(k₀))−Ĵ({circumflex over (ƒ)}₀₁(k₀)=k₀({circumflex over (ƒ)}₀₂(k₀)−{circumflex over (ƒ)}₀₁(k₀)); and determining the probability of occurrence for n₁ and n₂ as λ and 1−λ, respectively, using the equation $\lambda = {\frac{{{\hat{f}}_{02}\left( k_{0} \right)} - P_{FA}^{x}}{{{\hat{f}}_{02}\left( k_{0} \right)} - {{\hat{f}}_{01}\left( k_{0} \right)}}.}$
 6. A method in reducing the probability of error in non-Gaussian noise, comprising the steps of: recording an observed data process; determining with a processor stochastic resonance noise by identifying a known data probability density function for said data process and determining from said known probability density function of said observed data process, wherein the stochastic resonance noise probability density function that consists of a single delta function, δ(n−n₀) with value n₀ equal to a delta function location with probability one; adding said stochastic resonance noise to the data of the recorded data process; and displaying the results of adding said stochastic noise to said data.
 7. The method of claim 6, further comprising the step of determining a minimum probability of error using $\begin{matrix} {{P_{e,\min} = {\pi_{1}\left\lbrack {1 - {\max\limits_{f_{0}}{G\left( {f_{0},\frac{\pi_{0}}{\pi_{1}}} \right)}}} \right\rbrack}},} \\ {{{where}\mspace{14mu}{G\left( {f_{0},k} \right)}} = {{J\left( f_{0} \right)} - {kf}_{0}}} \\ {= {P_{D} - {{kP}_{FA}.}}} \end{matrix}$
 8. The method of claim 7, wherein the stochastic resonance noise probability density function consists of a single delta function located at n₀ and is calculated according to n₀=F₀ ⁻¹(ƒ₀), where ƒ₀ is the value the maximizes ${G\left( {f_{0},\frac{\pi_{0}}{\pi_{1}}} \right)}.$ 